Deutsche Forschungsberichte (DtF)

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Browsing Deutsche Forschungsberichte (DtF) by Subject "Final project report"

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    DFG Final Report for Automatic Fact Checking for Biomedical Information in Social Media and Scientific Literature (FIBISS), project number 667374
    (Hannover : Technische Informationsbibliothek, 2025-04-10) Klinger, Roman; Wührl, Amelie
    Research into methods for the automatic verification of facts, i.e., computational models that can distinguish correct information from misinformation or disinformation, is largely focused on the news domain and on the analysis of posts in social media. Among other things, texts are checked for their truthfulness. This can be done by analyzing linguistic features that suggest an intention to deceive or by comparing them with other sources that make comparable statements in terms of content. Most studies focus on politically relevant areas. The biomedical domain is also an area of particular social relevance. In social media, various actors and medical laypersons share reports on treatment methods, successes and failures, such as the (disproven) method of treating viral infections with deworming agents or disinfectants. There are also reports on (disproven) links between treatments and adverse effects, such as the causation of autism by vaccination. However, the biomedical domain, unlike other areas relevant for automated fact checking, benefits from a large resource of reliable scientific articles. The aim of the FIBISS project was therefore to develop and evaluate methods that can extract biomedical claims in social media and compare them with reliable sources. One challenge here is that social media does not typically use technical language, so different vocabularies have to be combined. The approach in FIBISS was therefore to develop generalizing information extraction methods. In the course of the project, large language models also became prominent as a further methodological approach. The project was therefore adapted to optimize general representations of claims in such a way that they are suitable for comparison using automatic fact-checking procedures. As a result, we contribute text corpora that are used to develop and evaluate automated biomedical fact-checking systems. We propose methods that automatically reformulate claims so that they are suitable to be automatically verified. Furthermore, we present approaches that can automatically assess the credibility of claims, even independently of existing evidence.